skills-audit

To audit AI agent skills for security risks, you analyze skill artifacts, prompts, and dependencies to detect vulnerabilities like prompt injection and data leaks. The audit engine combines static pattern matching with AI semantic risk assessment to return structured remediation guidance.

AI semantic risk assessment is a mechanism that evaluates the contextual meaning and behavior of prompts and code to identify subtle threats. Unlike basic static pattern matching, it detects complex vulnerabilities such as indirect prompt injection and unauthorized data exfiltration attempts.

To validate third-party AI skills for malware and supply-chain weaknesses, run the target skill directory through the audit engine. It performs comprehensive artifact analysis and threat-hunting across external references to generate a prioritized risk score and actionable fixes.

Yes, you can enforce security compliance policies for AI agent skills by auditing artifacts against established threat intelligence. The process identifies policy violations across code and dependencies, returning a structured risk assessment with prioritized findings to ensure compliance.

The best way to detect data leak vulnerabilities in AI prompts is using an audit engine that combines deep code understanding with semantic risk analysis. This approach identifies unauthorized external references and data exfiltration paths, providing prioritized remediation recommendations.

Static pattern matching alone often misses complex AI semantic risks like indirect prompt injection because it lacks contextual understanding of prompt behavior. Combining static analysis with deep semantic threat intelligence is necessary to identify nuanced vulnerabilities and supply-chain weaknesses.